Pentaho & Mongodb Partner to Solve Government Big Data Challenges

Pentaho & MongoDB Partner to Solve Government Big Data Challenges

December 2013

Bob Gourley Publisher, CTOvision.com Will LaForest Director of Federal, MongoDB Dave Henry SVP Enterprise Solutions, Pentaho

Bob Gourley Publisher, CTOvision.com

2 © 2013, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-7555 Brief Purpose Research & Reports Intro to top 5 A focus on a “Best new discipline Practices” of “Big Data Management” of Federal Data activities

Invitation to A perpetual Contribute your collaborate draft - your thoughts at and refine input is CTOvision.com approaches requested

. Big Data Government Newsletter - reader survey  2,600 readers  2% response rate, across Federal agencies . Review of openly published research by Wikibon, TDWI, IDC, Gartner, Forrester and of course our own CTOvision . Review of best practices and use cases from the best vendors in Enterprise Big Data . Engagement of the community at events like Strata and Hadoop World

Planning Assumption The ability to collect, parse, analyze machine data in real time, whether on premise or in the cloud, will continue to grow

. Agencies are thinking through the right changes to concepts and technologies . Old approaches still important, but cannot solve emerging problems . Big Data Management is an evolved discipline which builds on existing data management approaches to leverage new concepts, technologies and best practices to optimize mission support

• Open Source Information: analysis and integration • Situational Awareness across disparate data sets • Two use cases: “Connect the Dots” and “Needle in Haystack” • Cyber Security: rapid real time analysis of all relevant data • Asset catalog across extensive/dynamic enterprises • Rapid return of geospatial data • Location based push of data • Real time return of relevant search • Real time suggestion of topics • Bioinformatics: • Human Genome • Patient location, treatment, outcomes • Law Enforcement: Predictive Policing • Data Hub: Unified storage, governance, security, functionality

Start with a mission-focused vision. This will vary by organization. Support VISION to mission will drive everything else. Consider that analytics and Big Data go together. Should prioritize and tackle challenges like: Changes to governance processes, right mix of skills for workforce, learning new technology, STRATEGY prioritizing which workload types will be handled by which part of the architecture. Know existing infrastructure and process with focus on: Understanding of legal/policy dynamics relevant to your agency, understanding of new KNOW capabilities available, current and required throughputs/capacities, types of workloads supported by each components in the architecture, available tech choices. Document and continuously improve. Architect to manage data in its original form. Include right mix of traditional and new in your design. Don’t DESIGN assume any one platform will be a solution. Architect to insulate applications and users from a variety of disparate big data platforms. Avoid custom coding wherever possible. Don’t let new Big Data Platforms EXECUTE become proprietary silos. ETL remains important. Ensure training for all based on job function. Don’t neglect your own training. Serve the analyst.

 Revisit your vision. As you do, ponder this: How can you leverage data to support your mission?

 Continue to study use-cases and exchange best practices. Dialog with others in and out of your sector. Great lessons are coming from other industries.

 Continue to engage with the broader community. Sign-up for our Government Big Data Weekly.

 Share your lessons learned.

E-mail: [email protected] Blog: http://ctovision.com Twitter: http://www.twitter.com/bobgourley Facebook, LinkedIn, etc: See the blog

Will LaForest Director of Federal, MongoDB

Online 1990 2000 2010

Operational & Real-time NoSQL RDBMS RDBMS RDBMS Datawarehouse OLAP/BI OLAP/BI Hadoop

Offline

Variety Agile Development • Unstructured data • Iterative • Semi-structured • Short development data cycles • Polymorphic data • New workloads

Volume & Velocity New Architectures • Petabytes of data • Horizontal scaling • Trillions of records • Commodity servers • Millions of queries per second • Cloud computing

General Document Open- Purpose Oriented Source

• Find Paul’s cars MongoDB Rich Queries • Find everybody in London with a car built between 1970 and 1980 { first_name: ‘Paul’, • Find all of the car owners within 5km of Geospatial surname: ‘Miller’, Trafalgar Sq. city: ‘London’, location: [45.123,47.232],

• Find all the cars described as having cars: [ Text Search leather seats { model: ‘Bentley’, year: 1973, value: 100000, … }, • Calculate the average value of Paul’s Aggregation car collection { model: ‘Rolls Royce’, year: 1965, value: 330000, … } • Secondary • Full Text Native Indexes • Compound • Hash } • • Geospatial Covering }

Applications

CRM, ERP, Collaboration, Mobile, BI Security &Security Auditing

Data Management

Online Data Offline Data RDBMS RDBMS Hadoop EDW

Management & Monitoring & Management Infrastructure OS & Virtualization, Compute, Storage, Network

Relational MongoDB

{ first_name: ‘Paul’, surname: ‘Miller’ city: ‘London’, location: [45.123,47.232], cars: [ { model: ‘Bentley’, year: 1973, value: 100000, … }, { model: ‘Rolls Royce’, year: 1965, value: 330000, … } ] }

MongoDB does not need any defined data schema. Every document could have different data

{name: “will”, {name: “jeff”, {name: “brendan”, eyes: “blue”, eyes: “blue”, aliases: [“el diablo”]} birthplace: “NY”, height: 72, aliases: [“bill”, “la boss: “ben”} ciacco”], {name: “matt”, gender: ”???”, pizza: “DiGiorno”, boss: ”ben”} {name: “ben”, height: 74, hat: ”yes”} boss: 555.555.1212}

• Increase or decrease capacity as you go • Automatic balancing • Optimized for commodity servers and cloud infrastructure

• Automated replication and failover • 0 down time with hardware failure and upgrades • Multi-data center support • Improved operational simplicity (e.g., HW swaps) • Data durability and consistency

Top 5 Marketing Government Top 5 Investment Firm Agency Bank Data Key/value 10+ fields, arrays, 20+ fields, arrays, nested documents nested documents

Queries Key-based Compound queries Compound queries 1 – 100 docs/query Range queries Range queries 80/20 read/write MapReduce 50/50 read/write 20/80 read/write Servers ~250 ~50 ~40

Ops/sec 1,200,000 500,000 30,000

* These figures are provided as examples. Your application governs your performance.

• Application interacts with primaries • Analytical workloads on secondaries • Workloads are isolated from one another • Working set appropriate for each application

Real-time Real-time

Real-time

Primary:LON

Secondary:NYC

Secondary:SYD Primary:NYC

Secondary:LON

Secondary:SYD

Primary:SYD

Secondary:LON

Secondary:NYC

Dave Diegtel Head of Federal Sales, Pentaho

• Company and Product Maturity: Pentaho has been around for over 9 years, with 1,000’s of paid customers, and 5.0 Version release. Pentaho is proven and less risky.

• Business Model and Subscription: Pentaho’s Subscription Model and Server-based pricing allows for lower upfront investment and risk compared to legacy BI vendors who traditionally cost an average of 4X for similar size deployments.

• Government Certifications: Pentaho has made significant investments in Government Certifications and Compliance such as 508 and Security.

• Open API’s and extensible architecture enable ease of integration and reduce potential for vendor lock-in.

• Existing Government Customers and Cleared Personnel

A Comprehensive Big Data Platform

Dave Henry Senior VP Enterprise Solutions, Pentaho

Billing Customer

Existing & New Analytics Data Infrastructure Social Web & Processes Media

Location Network

ANY Data ANY Environment ANY Analytics • Relational • Data warehouses • Reports • Operational • Data marts • Dashboards • Big Data • Stack vendors • Visualizations • Data sources not yet • Cloud • Discovery anticipated… • Embedded • Predictive

Simplified Analytics Experience

Blended Big Data

Enterprise Big Data Integration

… • MongoDB delivers Scalable, Low-Latency Enterprise Data Store Mission Scope Pentaho Data Integration • Visual ETL development with Pentaho Analytics Pentaho Data Integration • Reporting • Dashboards (PDI) • Visualization • Discovery Pentaho Data • Reporting, Dashboards, Integration Visualization and Discovery with Pentaho Analytics

Customer Master

mongoDB PDI ETL cluster POS Data

Web Event Data

$push to data arrays

37 © 2013, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-7555 As CTO at Pentaho, James Dixon is responsible for Pentaho's architecture and technology roadmap. James has over 15 years of professional experience in software architecture, development and systems consulting. Prior to Pentaho, James held key technical roles at AppSource Corporation (acquired by Arbor Software which later merged into Hyperion Solutions) and Keyola (acquired by Lawson Software). Earlier in his career, James was a technology consultant working with large and small firms to deliver the benefits of James Dixon innovative technology in real-world Founder and CTO, Pentaho environments.

• Pentaho is the best platform to connect, integrate, and analyze both traditional sources and MongoDB

• Pentaho embraces and extends the MongoDB environment with rich visualization and exploration of data

• Pentaho’s Subscription-based business model lowers upfront investments, enabling faster ROI

• Pentaho has dozens of Federal Government Customers and made significant investments in government certifications and cleared personnel

• Pentaho and MongoDB are established partners – Pentaho carefully engineers its products to use the latest MongoDB APIs to provide the best possible performance

39 © 2013, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-7555 Next Steps and Q&A • Needs Assessment with Pentaho and MongoDB • Dave Diegtel - [email protected] • Will LaForrest - [email protected] • Try Pentaho (30 Free Trial) -- pentaho.com/download • Learn More about Big Data and Government Solutions • Pentaho • Big Data Website: pentahobigdata.com/ • Government Solutions: pentaho.com/solutions/government • MongoDB: • Government Solutions: mongodb.com/industries/government • Big Data: Examples and Guidelines for the Enterprise Decision Maker mongodb.com/lp/whitepaper/big-data-nosql • MongoDB Top 5 Considerations When Evaluating NoSQL Databases mongodb.com/lp/whitepaper/nosql-considerations • Sign-up for the Big Data Government Newsletter at CTOvision.com & take reader survey